1. Field of the Invention
The present invention is directed to a method for improving the quality, particularly the signal-to-noise ratio, of an image, of the type wherein image data are subjected to a mathematical operation in the position domain that improves the image quality by, for each picture element subject to the mathematical operation, picture elements in the environment of that picture element are provided respectively with independent, first weighting factors that have a magnitude dependent on the spatial distance of the respective picture element of the environment from the individual picture element, and the mathematical operation for that picture element is implemented with those weighting.
2. Description of the Prior Art
Images of exposure subjects often have a noise component that can make it more difficult to recognize details in the image. A low signal-to-noise ratio in the image has a negative effect specifically in the field of medical imaging such as, for example, magnetic resonance tomography or computed tomography since the recognition of image details plays a significant part in this field of application.
For improving a low signal-to-noise ratio of magnetic resonance images, it is known to apply low-pass filters to the digital image data. For this purpose, the image data are convoluted (convolved) with a suitable function, for example a Gauss function that forms a filter window. In this mathematical operation, picture elements in the environment of the individual picture element, for each individual picture element of the image are taken into consideration with mutually independent weighting factors formed by a weighting function that has a magnitude dependent on the spatial distance of the respective picture element of the environment from the individual picture element. As a result of the low-pass filtering, however, the spatial resolution of the image is reduced, so that important information about structure details of the examined body region in, for example, medical images, can be lost.
In addition to this simple low-pass filtering, a number of more complex methods are known for noise suppression that, however, frequently require considerable computing time. One known method, for example, is median filtering, but this does not provide satisfactory results for magnetic resonance images. Further, complicated segmenting approaches are known for the image processing in the medical field in order to detect and suitably process regions in the image containing similar image information.
Lim, J. S., Image Enhancement in Digital Image Processing Techniques, M. P. Ekstrom (Editor), Academic Press, 1984, pages 11–25 and 33–41, discloses methods for improving the quality of images wherein adaptive filter techniques are utilized. In one of these methods, the variance is calculated for a defined surrounding region of every individual picture element that is subjected to a low-pass filtering, and the window width of the filter function is set dependent on this variance. This, however, produces no noise suppression at sharp edges within the image, so that the image quality is not improved in these regions.